Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model
Faezeh Rohani,
Hamid Hassannia,
Mohammad Amin MoghaddasiFar,
Elham Sagheb
Issue:
Volume 2, Issue 4, August 2014
Pages:
52-56
Received:
19 August 2014
Accepted:
4 September 2014
Published:
20 September 2014
DOI:
10.11648/j.cbb.20140204.11
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Abstract: Automatic detection of human cell is still one of the most common investigation methods that may be used as part of a computer aided medical decision making system[1]. In this paper a statistical method based on Gaussian Mixture Model is applied to human cell detection in microscopic images[2]. 120 normal microscopic images of human cell from our research laboratory were used for analysis. Texture and grayscale features extracted from blocks of these images are given to Gaussian Mixture Model as input. It is used to model this data into three classes which are cell, extra cellular space and cell membrane [3]. Our proposed algorithm is applied on a sample dataset and experimental results show that this model is both accurate and fast with overall detection rate of around 91.23%. Error rate for cell detection was 1.82%.
Abstract: Automatic detection of human cell is still one of the most common investigation methods that may be used as part of a computer aided medical decision making system[1]. In this paper a statistical method based on Gaussian Mixture Model is applied to human cell detection in microscopic images[2]. 120 normal microscopic images of human cell from our r...
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Application of Hypercorrelated Matrices in Ecological Research
Branko Karadžić,
Snežana Jarić,
Pavle Pavlović,
Saša Marinković,
Miroslava Mitrović
Issue:
Volume 2, Issue 4, August 2014
Pages:
57-62
Received:
18 August 2014
Accepted:
11 September 2014
Published:
30 September 2014
DOI:
10.11648/j.cbb.20140204.12
Downloads:
Views:
Abstract: Ecological data matrices often require some form of pre-processing so that any undesirable effects (e.g. the variable size effect) may be removed from multivariate analyses. This paper describes hypercorrelation, a simple data transformation that improves ordination methods significantly. Hypercorrelated matrices efficiently eliminate the ‘arch’ (or Guttman) effect, a spurious polynomial relation between ordination axes. These matrices reduce the sensitivity of correspondence analysis to outliers. Canonical analyses (canonical correspondence analysis and redundancy analysis) of hypercorrelated matrices are resistant to undesirable effects of missing data. Finally, the hypercorrelation extends applicability of “linear ordination method” (principal components analysis and redundancy analysis) to sparse (high beta diversity) matrices.
Abstract: Ecological data matrices often require some form of pre-processing so that any undesirable effects (e.g. the variable size effect) may be removed from multivariate analyses. This paper describes hypercorrelation, a simple data transformation that improves ordination methods significantly. Hypercorrelated matrices efficiently eliminate the ‘arch’ (o...
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